{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Iterative Detection and Decoding\n",
    "In this notebook, you will learn how to set-up an iterative detection and decoding (IDD) scheme (first presented in [1]) by combining multiple available components in Sionna.\n",
    "\n",
    "For a gentle introduction to MIMO simulations, we refer to the notebooks [\"Simple MIMO Simulations\"](https://nvlabs.github.io/sionna/phy/tutorials/Simple_MIMO_Simulation.html) and [\"MIMO OFDM Transmissions over CDL\"](https://nvlabs.github.io/sionna/phy/tutorials/MIMO_OFDM_Transmissions_over_CDL.html).\n",
    "\n",
    "You will evaluate the performance of IDD with OFDM MIMO detection and soft-input soft-output (SISO) LDPC decoding and compare it againts several non-iterative detectors, such as soft-output LMMSE, K-Best, and expectation propagation (EP), as well as iterative SISO MMSE-PIC detection [2].\n",
    "\n",
    "For the non-IDD models, the signal processing pipeline looks as follows:"
   ]
  },
  {
   "attachments": {
    "block_diagram.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![block_diagram.png](attachment:block_diagram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterative Detection and Decoding\n",
    "The IDD MIMO receiver iteratively exchanges soft-information between the data detector and the channel decoder, which works as follows:"
   ]
  },
  {
   "attachments": {
    "idd_diagram.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idd_diagram.png](attachment:idd_diagram.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We denote by $\\mathrm{L}^{D}$ the *a posteriori* information (represented by log-likelihood ratios, LLRs) and by $\\mathrm{L}^{E} = \\mathrm{L}^{D} - \\mathrm{L}^{A}$ the extrinsic information, which corresponds to the information gain in $\\mathrm{L}^{D}$ relative to the *a priori* information $\\mathrm{L}^{A}$. The *a priori* LLRs represent soft information, provided to either the input of the detector (i.e., $\\mathrm{L}^{A}_{Det}$) or the decoder (i.e., $\\mathrm{L}^{A}_{Dec}$). While exchanging extrinsic information is standard for classical IDD, the SISO MMSE-PIC detector [2] turned out to work better when provided with the full *a posteriori* information from the decoder.\n",
    "\n",
    "Originally, IDD was proposed with a resetting (Turbo) decoder [1]. However, state-of-the-art IDD with LDPC message passing decoding showed better performance with a non-resetting decoder [3], particularly for a low number of decoding iterations. Therefore, we will forward the decoder state (i.e., the check node to variable node messages) from each IDD iteration to the next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n",
    "* [Simulation Parameters](#Simulation-Parameters)\n",
    "* [Setting-up an End-to-end Block](#Setting-up-an-end-to-end-Block)\n",
    "* [Non-IDD versus IDD Benchmarks](#Non-IDD-versus-IDD-Benchmarks)\n",
    "* [Discussion-Optimizing IDD with Machine Learning](#Discussion-Optimizing-IDD-with-Machine-Learning)\n",
    "* [Comments](#Comments)\n",
    "* [List of References](#List-of-References)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GPU Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-10T21:58:50.651342Z",
     "iopub.status.busy": "2025-03-10T21:58:50.650815Z",
     "iopub.status.idle": "2025-03-10T21:58:53.164996Z",
     "shell.execute_reply": "2025-03-10T21:58:53.164041Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n",
    "    gpu_num = 0 # Use \"\" to use the CPU\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "import tensorflow as tf\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        tf.config.experimental.set_memory_growth(gpus[0], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "# Avoid warnings from TensorFlow\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sionna.phy import Block\n",
    "from sionna.phy.mimo import StreamManagement\n",
    "from sionna.phy.utils import sim_ber, ebnodb2no, expand_to_rank\n",
    "from sionna.phy.mapping import Mapper, Constellation, BinarySource\n",
    "from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n",
    "                            LinearDetector, KBestDetector, EPDetector, \\\n",
    "                            RemoveNulledSubcarriers, MMSEPICDetector\n",
    "from sionna.phy.channel import OFDMChannel, RayleighBlockFading, gen_single_sector_topology\n",
    "from sionna.phy.channel.tr38901 import UMa, Antenna, PanelArray\n",
    "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n",
    "\n",
    "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Parameters\n",
    "In the following, we set the simulation parameters. Please modify at will; adapting the batch size to your hardware setup might be beneficial.\n",
    "\n",
    "The standard configuration implements a coded 5G inspired MU-MIMO OFDM uplink transmission over 3GPP UMa channels, with 4 single-antenna UEs, 16-QAM modulation, and a 16 element dual-polarized uniform planar antenna array (UPA) at the gNB. We implement least squares channel estimation with linear interpolation. Alternatively, we implement iid Rayleigh fading channels and perfect channel state information (CSI), which can be controlled by the model parameter `perfect_csi_rayleigh`.\n",
    "As channel code, we apply a rate-matched 5G LDPC code at rate 1/2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-10T21:58:53.169228Z",
     "iopub.status.busy": "2025-03-10T21:58:53.168824Z",
     "iopub.status.idle": "2025-03-10T21:58:53.872158Z",
     "shell.execute_reply": "2025-03-10T21:58:53.871557Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SIMPLE_SIM = False   # reduced simulation time for simple simulation if set to True\n",
    "if SIMPLE_SIM:\n",
    "    batch_size = int(1e1)  # number of OFDM frames to be analyzed per batch\n",
    "    num_iter = 5  # number of Monte Carlo Iterations (total number of Monte Carlo trials is num_iter*batch_size)\n",
    "    num_steps = 6\n",
    "    tf.config.run_functions_eagerly(True)   # run eagerly for better debugging\n",
    "else:\n",
    "    batch_size = int(64)  # number of OFDM frames to be analyzed per batch\n",
    "    num_iter = 128  # number of Monte Carlo Iterations (total number of Monte Carlo trials is num_iter*batch_size)\n",
    "    num_steps = 11\n",
    "\n",
    "ebno_db_min_perf_csi = -10  # min EbNo value in dB for perfect csi benchmarks\n",
    "ebno_db_max_perf_csi = 0\n",
    "ebno_db_min_cest = -10\n",
    "ebno_db_max_cest = 10\n",
    "\n",
    "\n",
    "NUM_OFDM_SYMBOLS = 14\n",
    "FFT_SIZE = 12*4 # 4 PRBs\n",
    "SUBCARRIER_SPACING = 30e3 # Hz\n",
    "CARRIER_FREQUENCY = 3.5e9 # Hz\n",
    "SPEED = 3. # m/s\n",
    "num_bits_per_symbol = 4 # 16 QAM\n",
    "n_ue = 4 # 4 UEs\n",
    "NUM_RX_ANT = 16 # 16 BS antennas\n",
    "num_pilot_symbols = 2\n",
    "\n",
    "# The user terminals (UTs) are equipped with a single antenna\n",
    "# with vertial polarization.\n",
    "UT_ANTENNA = Antenna(polarization='single',\n",
    "                     polarization_type='V',\n",
    "                     antenna_pattern='omni', # Omnidirectional antenna pattern\n",
    "                     carrier_frequency=CARRIER_FREQUENCY)\n",
    "\n",
    "# The base station is equipped with an antenna\n",
    "# array of 8 cross-polarized antennas,\n",
    "# resulting in a total of 16 antenna elements.\n",
    "BS_ARRAY = PanelArray(num_rows_per_panel=2,\n",
    "                      num_cols_per_panel=4,\n",
    "                      polarization='dual',\n",
    "                      polarization_type='cross',\n",
    "                      antenna_pattern='38.901', # 3GPP 38.901 antenna pattern\n",
    "                      carrier_frequency=CARRIER_FREQUENCY)\n",
    "\n",
    "# 3GPP UMa channel model is considered\n",
    "channel_model_uma = UMa(carrier_frequency=CARRIER_FREQUENCY,\n",
    "                    o2i_model='low',\n",
    "                    ut_array=UT_ANTENNA,\n",
    "                    bs_array=BS_ARRAY,\n",
    "                    direction='uplink',\n",
    "                    enable_shadow_fading=False,\n",
    "                    enable_pathloss=False)\n",
    "\n",
    "channel_model_rayleigh = RayleighBlockFading(num_rx=1, num_rx_ant=NUM_RX_ANT, num_tx=n_ue, num_tx_ant=1)\n",
    "\n",
    "constellation = Constellation(\"qam\", num_bits_per_symbol=num_bits_per_symbol)\n",
    "\n",
    "rx_tx_association = np.ones([1, n_ue])\n",
    "sm = StreamManagement(rx_tx_association, 1)\n",
    "\n",
    "# Parameterize the OFDM channel\n",
    "rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS, pilot_ofdm_symbol_indices = [2, 11],\n",
    "                  fft_size=FFT_SIZE, num_tx=n_ue,\n",
    "                  pilot_pattern = \"kronecker\",\n",
    "                  subcarrier_spacing=SUBCARRIER_SPACING)\n",
    "\n",
    "rg.show()\n",
    "plt.show()\n",
    "\n",
    "# Parameterize the LDPC code\n",
    "R = 0.5  # rate 1/2\n",
    "N = int(FFT_SIZE * (NUM_OFDM_SYMBOLS - 2) * num_bits_per_symbol)\n",
    "# N = int((FFT_SIZE) * (NUM_OFDM_SYMBOLS - 2) * num_bits_per_symbol)\n",
    "# code length; - 12 because of 11 guard carriers and 1 DC carrier, - 2 becaues of 2 pilot symbols\n",
    "K = int(N * R)  # number of information bits per codeword\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting-up an End-to-end Block\n",
    "\n",
    "Now, we define the baseline models for benchmarking. Let us start with the non-IDD models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-10T21:58:53.877044Z",
     "iopub.status.busy": "2025-03-10T21:58:53.876824Z",
     "iopub.status.idle": "2025-03-10T21:58:53.891099Z",
     "shell.execute_reply": "2025-03-10T21:58:53.890334Z"
    }
   },
   "outputs": [],
   "source": [
    "class NonIddModel(Block):\n",
    "    def __init__(self, num_bp_iter=12, detector='lmmse', cest_type=\"LS\", interp=\"lin\", perfect_csi_rayleigh=False):\n",
    "        super().__init__()\n",
    "        self._num_bp_iter = int(num_bp_iter)\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(K, N, num_bits_per_symbol=num_bits_per_symbol)\n",
    "        self._mapper = Mapper(constellation=constellation)\n",
    "        self._rg_mapper = ResourceGridMapper(rg)\n",
    "\n",
    "        # Channel\n",
    "        if perfect_csi_rayleigh:\n",
    "            self._channel_model = channel_model_rayleigh\n",
    "        else:\n",
    "            self._channel_model = channel_model_uma\n",
    "\n",
    "        self._channel = OFDMChannel(channel_model=self._channel_model,\n",
    "                                    resource_grid=rg,\n",
    "                                    add_awgn=True, normalize_channel=True, return_channel=True)\n",
    "\n",
    "        # Receiver\n",
    "        self._cest_type = cest_type\n",
    "        self._interp = interp\n",
    "\n",
    "        # Channel estimation\n",
    "        self._perfect_csi_rayleigh = perfect_csi_rayleigh\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            self._removeNulledSc = RemoveNulledSubcarriers(rg)\n",
    "        elif cest_type == \"LS\":\n",
    "            self._ls_est = LSChannelEstimator(rg, interpolation_type=interp)\n",
    "        else:\n",
    "            raise NotImplementedError('Not implemented:' + cest_type)\n",
    "\n",
    "        # Detection\n",
    "        if detector == \"lmmse\":\n",
    "            self._detector = LinearDetector(\"lmmse\", 'bit', \"maxlog\", rg, sm, constellation_type=\"qam\",\n",
    "                                            num_bits_per_symbol=num_bits_per_symbol, hard_out=False)\n",
    "        elif detector == \"k-best\":\n",
    "            k = 64\n",
    "            self._detector = KBestDetector('bit', n_ue, k, rg, sm, constellation_type=\"qam\",\n",
    "                                           num_bits_per_symbol=num_bits_per_symbol, hard_out=False)\n",
    "        elif detector == \"ep\":\n",
    "            l = 10\n",
    "            self._detector = EPDetector('bit', rg, sm, num_bits_per_symbol, l=l, hard_out=False)\n",
    "\n",
    "        # Forward error correction (decoder)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, return_infobits=True, hard_out=True, num_iter=num_bp_iter, cn_update='minsum')\n",
    "\n",
    "    def new_topology(self, batch_size):\n",
    "        \"\"\"Set new topology\"\"\"\n",
    "        if isinstance(self._channel_model, UMa):\n",
    "            # sensible values according to 3GPP standard, no mobility by default\n",
    "            topology = gen_single_sector_topology(batch_size,\n",
    "                                                  n_ue, max_ut_velocity=SPEED,\n",
    "                                                  scenario=\"uma\")\n",
    "            self._channel_model.set_topology(*topology)\n",
    "\n",
    "    @tf.function  # We don't use jit_compile=True to ensure better numerical stability\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        self.new_topology(batch_size)\n",
    "\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        no = ebnodb2no(ebno_db=ebno_db, num_bits_per_symbol=num_bits_per_symbol,\n",
    "                       coderate=R)  # normalize in OFDM freq. domain\n",
    "        b = self._binary_source([batch_size, n_ue, 1, K])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)\n",
    "        x_rg = self._rg_mapper(x)\n",
    "\n",
    "        ######################################\n",
    "        ## Channel\n",
    "        # A batch of new channel realizations is sampled and applied at every inference\n",
    "        no_ = expand_to_rank(no, tf.rank(x_rg))\n",
    "        y, h = self._channel(x_rg, no_)\n",
    "\n",
    "        ######################################\n",
    "        ## Receiver\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            h_hat = self._removeNulledSc(h)\n",
    "            chan_est_var = tf.zeros(tf.shape(h_hat),\n",
    "                                    dtype=tf.float32)  # No channel estimation error when perfect CSI knowledge is assumed\n",
    "        else:\n",
    "            h_hat, chan_est_var = self._ls_est(y, no)\n",
    "\n",
    "        llr_ch = self._detector(y, h_hat, chan_est_var, no)  # detector\n",
    "        b_hat = self._decoder(llr_ch)\n",
    "        return b, b_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we implement the IDD model with a non-resetting LDPC decoder, as in [3], i.e., we forward the LLRs and decoder state from one IDD iteration to the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-10T21:58:53.895048Z",
     "iopub.status.busy": "2025-03-10T21:58:53.894793Z",
     "iopub.status.idle": "2025-03-10T21:58:53.907525Z",
     "shell.execute_reply": "2025-03-10T21:58:53.906710Z"
    }
   },
   "outputs": [],
   "source": [
    "class IddModel(NonIddModel):  # inherited from NonIddModel\n",
    "    def __init__(self, num_idd_iter=3, num_bp_iter_per_idd_iter=12, cest_type=\"LS\", interp=\"lin\", perfect_csi_rayleigh=False):\n",
    "        super().__init__(num_bp_iter=num_bp_iter_per_idd_iter, detector=\"lmmse\", cest_type=cest_type,\n",
    "                         interp=interp, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "        # first IDD detector is LMMSE as MMSE-PIC with zero-prior bils down to soft-output LMMSE\n",
    "        self._num_idd_iter = num_idd_iter\n",
    "        self._siso_detector = MMSEPICDetector(output=\"bit\", resource_grid=rg, stream_management=sm,\n",
    "                                              demapping_method='maxlog', constellation=constellation, num_iter=1,\n",
    "                                              hard_out=False)\n",
    "        self._siso_decoder = LDPC5GDecoder(self._encoder, return_infobits=False,\n",
    "                                           num_iter=num_bp_iter_per_idd_iter, return_state=True, hard_out=False, cn_update='minsum')\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, return_infobits=True, return_state=True, hard_out=True, num_iter=num_bp_iter_per_idd_iter, cn_type='minsum')\n",
    "        # last decoder must also be statefull\n",
    "\n",
    "    @tf.function  # We don't use jit_compile=True to ensure better numerical stability\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        self.new_topology(batch_size)\n",
    "\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        no = ebnodb2no(ebno_db=ebno_db, num_bits_per_symbol=num_bits_per_symbol,\n",
    "                       coderate=R)  # normalize in OFDM freq. domain\n",
    "        b = self._binary_source([batch_size, n_ue, 1, K])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)\n",
    "        x_rg = self._rg_mapper(x)\n",
    "\n",
    "        ######################################\n",
    "        ## Channel\n",
    "        # A batch of new channel realizations is sampled and applied at every inference\n",
    "        no_ = expand_to_rank(no, tf.rank(x_rg))\n",
    "        y, h = self._channel(x_rg, no_)\n",
    "\n",
    "        ######################################\n",
    "        ## Receiver\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            h_hat = self._removeNulledSc(h)\n",
    "            chan_est_var = tf.zeros(tf.shape(h_hat),\n",
    "                                    dtype=tf.float32)  # No channel estimation error when perfect CSI knowledge is assumed\n",
    "        else:\n",
    "            h_hat, chan_est_var = self._ls_est(y, no)\n",
    "\n",
    "        llr_ch = self._detector(y, h_hat, chan_est_var, no)  # soft-output LMMSE detection\n",
    "        msg_v2c = None\n",
    "\n",
    "        if self._num_idd_iter >= 2:\n",
    "            # perform first iteration outside the while_loop to initialize msg_v2c\n",
    "            [llr_dec, msg_v2c] = self._siso_decoder(llr_ch, msg_v2c=msg_v2c)\n",
    "            # forward a posteriori information from decoder\n",
    "\n",
    "            llr_ch = self._siso_detector(y, h_hat, llr_dec, chan_est_var, no)\n",
    "            # forward extrinsic information\n",
    "\n",
    "            def idd_iter(llr_ch, msg_v2c, it):\n",
    "                [llr_dec, msg_v2c] = self._siso_decoder(llr_ch, msg_v2c=msg_v2c)\n",
    "                # forward a posteriori information from decoder\n",
    "                llr_ch = self._siso_detector(y, h_hat, llr_dec, chan_est_var, no)\n",
    "                # forward extrinsic information from detector\n",
    "\n",
    "                it += 1\n",
    "                return llr_ch, msg_v2c, it\n",
    "\n",
    "            def idd_stop(llr_ch, msg_v2c, it):\n",
    "                return tf.less(it, self._num_idd_iter - 1)\n",
    "\n",
    "            it = tf.constant(1)     # we already performed initial detection and one full iteration\n",
    "            llr_ch, msg_v2c, it = tf.while_loop(idd_stop, idd_iter, (llr_ch, msg_v2c, it), parallel_iterations=1,\n",
    "                                               maximum_iterations=self._num_idd_iter - 1)\n",
    "        else:\n",
    "            # non-idd\n",
    "            pass\n",
    "\n",
    "        [b_hat, _] = self._decoder(llr_ch, msg_v2c=msg_v2c)    # final hard-output decoding (only returning information bits)\n",
    "        return b, b_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-IDD versus IDD Benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-10T21:58:53.911105Z",
     "iopub.status.busy": "2025-03-10T21:58:53.910890Z",
     "iopub.status.idle": "2025-03-10T22:23:11.992789Z",
     "shell.execute_reply": "2025-03-10T22:23:11.991924Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1223e-01 | 1.0000e+00 |      250351 |     1179648 |         1024 |        1024 |         7.0 |reached target block errors\n",
      "     -9.0 | 1.8603e-01 | 1.0000e+00 |      219449 |     1179648 |         1024 |        1024 |         0.2 |reached target block errors\n",
      "     -8.0 | 1.1066e-01 | 9.7168e-01 |      130538 |     1179648 |          995 |        1024 |         0.2 |reached target block errors\n",
      "     -7.0 | 1.6797e-02 | 3.4297e-01 |       49537 |     2949120 |          878 |        2560 |         0.5 |reached target block errors\n",
      "     -6.0 | 1.1418e-03 | 3.1675e-02 |       34009 |    29786112 |          819 |       25856 |         5.2 |reached target block errors\n",
      "     -5.0 | 8.7447e-05 | 2.2888e-03 |        3301 |    37748736 |           75 |       32768 |         6.6 |reached max iterations\n",
      "     -4.0 | 2.6491e-08 | 3.0518e-05 |           1 |    37748736 |            1 |       32768 |         6.6 |reached max iterations\n",
      "     -3.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |         6.6 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -3.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1020e-01 | 1.0000e+00 |      247962 |     1179648 |         1024 |        1024 |         3.8 |reached target block errors\n",
      "     -9.0 | 1.8366e-01 | 1.0000e+00 |      216660 |     1179648 |         1024 |        1024 |         0.3 |reached target block errors\n",
      "     -8.0 | 9.6857e-02 | 9.5020e-01 |      114257 |     1179648 |          973 |        1024 |         0.3 |reached target block errors\n",
      "     -7.0 | 1.2495e-02 | 3.0256e-01 |       40534 |     3244032 |          852 |        2816 |         0.8 |reached target block errors\n",
      "     -6.0 | 4.7647e-04 | 1.7700e-02 |       17986 |    37748736 |          580 |       32768 |         9.2 |reached max iterations\n",
      "     -5.0 | 3.5233e-06 | 4.2725e-04 |         133 |    37748736 |           14 |       32768 |         9.2 |reached max iterations\n",
      "     -4.0 | 5.2982e-08 | 3.0518e-05 |           2 |    37748736 |            1 |       32768 |         9.2 |reached max iterations\n",
      "     -3.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |         9.2 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -3.0 dB.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1202e-01 | 1.0000e+00 |      250105 |     1179648 |         1024 |        1024 |         7.4 |reached target block errors\n",
      "     -9.0 | 1.8481e-01 | 1.0000e+00 |      218011 |     1179648 |         1024 |        1024 |         3.3 |reached target block errors\n",
      "     -8.0 | 1.1646e-01 | 9.9707e-01 |      137385 |     1179648 |         1021 |        1024 |         3.3 |reached target block errors\n",
      "     -7.0 | 2.2363e-02 | 5.9375e-01 |       39571 |     1769472 |          912 |        1536 |         5.0 |reached target block errors\n",
      "     -6.0 | 1.0629e-03 | 5.2894e-02 |       19122 |    17989632 |          826 |       15616 |        50.9 |reached target block errors\n",
      "     -5.0 | 2.6385e-05 | 1.1902e-03 |         996 |    37748736 |           39 |       32768 |       106.7 |reached max iterations\n",
      "     -4.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |       106.7 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -4.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 1.4904e-01 | 1.0000e+00 |      175813 |     1179648 |         1024 |        1024 |         6.0 |reached target block errors\n",
      "     -9.0 | 9.4156e-02 | 9.9609e-01 |      111071 |     1179648 |         1020 |        1024 |         0.3 |reached target block errors\n",
      "     -8.0 | 1.7746e-02 | 5.1395e-01 |       36635 |     2064384 |          921 |        1792 |         0.6 |reached target block errors\n",
      "     -7.0 | 4.4343e-04 | 2.2675e-02 |       16739 |    37748736 |          743 |       32768 |        10.2 |reached max iterations\n",
      "     -6.0 | 3.8412e-06 | 3.9673e-04 |         145 |    37748736 |           13 |       32768 |        10.2 |reached max iterations\n",
      "     -5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        10.2 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 1.5042e-01 | 1.0000e+00 |      177440 |     1179648 |         1024 |        1024 |         5.0 |reached target block errors\n",
      "     -9.0 | 9.6599e-02 | 9.9609e-01 |      113953 |     1179648 |         1020 |        1024 |         0.4 |reached target block errors\n",
      "     -8.0 | 1.7984e-02 | 4.4824e-01 |       42429 |     2359296 |          918 |        2048 |         0.9 |reached target block errors\n",
      "     -7.0 | 3.8775e-04 | 1.2695e-02 |       14637 |    37748736 |          416 |       32768 |        14.5 |reached max iterations\n",
      "     -6.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        14.5 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -6.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.9921e-01 | 1.0000e+00 |      352961 |     1179648 |         1024 |        1024 |        14.4 |reached target block errors\n",
      "     -8.0 | 2.6382e-01 | 1.0000e+00 |      311216 |     1179648 |         1024 |        1024 |         0.3 |reached target block errors\n",
      "     -6.0 | 2.2769e-01 | 1.0000e+00 |      268590 |     1179648 |         1024 |        1024 |         0.4 |reached target block errors\n",
      "     -4.0 | 1.5128e-01 | 9.1797e-01 |      178461 |     1179648 |          940 |        1024 |         0.4 |reached target block errors\n",
      "     -2.0 | 4.9773e-02 | 4.0186e-01 |      117429 |     2359296 |          823 |        2048 |         0.7 |reached target block errors\n",
      "      0.0 | 1.1782e-02 | 1.0046e-01 |      111188 |     9437184 |          823 |        8192 |         2.9 |reached target block errors\n",
      "      2.0 | 2.7987e-03 | 2.4963e-02 |      105649 |    37748736 |          818 |       32768 |        11.5 |reached max iterations\n",
      "      4.0 | 1.2508e-03 | 9.2163e-03 |       47215 |    37748736 |          302 |       32768 |        11.5 |reached max iterations\n",
      "      6.0 | 1.0336e-03 | 6.5308e-03 |       39017 |    37748736 |          214 |       32768 |        11.5 |reached max iterations\n",
      "      8.0 | 8.7741e-04 | 5.5847e-03 |       33121 |    37748736 |          183 |       32768 |        11.4 |reached max iterations\n",
      "     10.0 | 8.2003e-04 | 4.7302e-03 |       30955 |    37748736 |          155 |       32768 |        11.4 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.9983e-01 | 1.0000e+00 |      353698 |     1179648 |         1024 |        1024 |         7.5 |reached target block errors\n",
      "     -8.0 | 2.6482e-01 | 1.0000e+00 |      312397 |     1179648 |         1024 |        1024 |         0.5 |reached target block errors\n",
      "     -6.0 | 2.2826e-01 | 1.0000e+00 |      269264 |     1179648 |         1024 |        1024 |         0.4 |reached target block errors\n",
      "     -4.0 | 1.4842e-01 | 9.1211e-01 |      175085 |     1179648 |          934 |        1024 |         0.4 |reached target block errors\n",
      "     -2.0 | 4.1157e-02 | 3.5039e-01 |      121377 |     2949120 |          897 |        2560 |         1.1 |reached target block errors\n",
      "      0.0 | 7.4100e-03 | 6.9548e-02 |      100524 |    13565952 |          819 |       11776 |         5.0 |reached target block errors\n",
      "      2.0 | 2.1057e-03 | 1.6449e-02 |       79486 |    37748736 |          539 |       32768 |        14.1 |reached max iterations\n",
      "      4.0 | 1.0968e-03 | 7.5684e-03 |       41402 |    37748736 |          248 |       32768 |        14.0 |reached max iterations\n",
      "      6.0 | 7.8922e-04 | 5.6458e-03 |       29792 |    37748736 |          185 |       32768 |        14.0 |reached max iterations\n",
      "      8.0 | 1.0145e-03 | 6.3782e-03 |       38296 |    37748736 |          209 |       32768 |        13.9 |reached max iterations\n",
      "     10.0 | 8.4445e-04 | 5.8594e-03 |       31877 |    37748736 |          192 |       32768 |        13.9 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0681e-01 | 1.0000e+00 |      361922 |     1179648 |         1024 |        1024 |        10.8 |reached target block errors\n",
      "     -8.0 | 2.7050e-01 | 1.0000e+00 |      319091 |     1179648 |         1024 |        1024 |         3.4 |reached target block errors\n",
      "     -6.0 | 2.3325e-01 | 1.0000e+00 |      275147 |     1179648 |         1024 |        1024 |         3.5 |reached target block errors\n",
      "     -4.0 | 1.5728e-01 | 9.8145e-01 |      185530 |     1179648 |         1005 |        1024 |         3.5 |reached target block errors\n",
      "     -2.0 | 4.7583e-02 | 4.2969e-01 |      112262 |     2359296 |          880 |        2048 |         7.0 |reached target block errors\n",
      "      0.0 | 6.2228e-03 | 6.2425e-02 |       95429 |    15335424 |          831 |       13312 |        45.3 |reached target block errors\n",
      "      2.0 | 1.3220e-03 | 1.0773e-02 |       49902 |    37748736 |          353 |       32768 |       111.2 |reached max iterations\n",
      "      4.0 | 7.6869e-04 | 4.7302e-03 |       29017 |    37748736 |          155 |       32768 |       111.3 |reached max iterations\n",
      "      6.0 | 5.9123e-04 | 3.8757e-03 |       22318 |    37748736 |          127 |       32768 |       111.1 |reached max iterations\n",
      "      8.0 | 6.0389e-04 | 4.1809e-03 |       22796 |    37748736 |          137 |       32768 |       111.4 |reached max iterations\n",
      "     10.0 | 8.1682e-04 | 5.1575e-03 |       30834 |    37748736 |          169 |       32768 |       111.2 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.7276e-01 | 1.0000e+00 |      321761 |     1179648 |         1024 |        1024 |         9.6 |reached target block errors\n",
      "     -8.0 | 2.2901e-01 | 1.0000e+00 |      270153 |     1179648 |         1024 |        1024 |         0.5 |reached target block errors\n",
      "     -6.0 | 1.7854e-01 | 1.0000e+00 |      210609 |     1179648 |         1024 |        1024 |         0.5 |reached target block errors\n",
      "     -4.0 | 9.0361e-02 | 7.8516e-01 |      133242 |     1474560 |         1005 |        1280 |         0.6 |reached target block errors\n",
      "     -2.0 | 1.8625e-02 | 1.9210e-01 |       93375 |     5013504 |          836 |        4352 |         2.0 |reached target block errors\n",
      "      0.0 | 3.3976e-03 | 2.9494e-02 |      109218 |    32145408 |          823 |       27904 |        12.8 |reached target block errors\n",
      "      2.0 | 1.0031e-03 | 8.1482e-03 |       37865 |    37748736 |          267 |       32768 |        15.0 |reached max iterations\n",
      "      4.0 | 8.0527e-04 | 4.9744e-03 |       30398 |    37748736 |          163 |       32768 |        15.0 |reached max iterations\n",
      "      6.0 | 5.6651e-04 | 3.6926e-03 |       21385 |    37748736 |          121 |       32768 |        15.0 |reached max iterations\n",
      "      8.0 | 7.8596e-04 | 5.8594e-03 |       29669 |    37748736 |          192 |       32768 |        15.0 |reached max iterations\n",
      "     10.0 | 7.3308e-04 | 5.7983e-03 |       27673 |    37748736 |          190 |       32768 |        15.1 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.7259e-01 | 1.0000e+00 |      321555 |     1179648 |         1024 |        1024 |        10.0 |reached target block errors\n",
      "     -8.0 | 2.2693e-01 | 1.0000e+00 |      267699 |     1179648 |         1024 |        1024 |         0.6 |reached target block errors\n",
      "     -6.0 | 1.7713e-01 | 1.0000e+00 |      208947 |     1179648 |         1024 |        1024 |         0.6 |reached target block errors\n",
      "     -4.0 | 9.3064e-02 | 7.7500e-01 |      137228 |     1474560 |          992 |        1280 |         0.8 |reached target block errors\n",
      "     -2.0 | 1.7267e-02 | 1.5110e-01 |      112030 |     6488064 |          851 |        5632 |         3.3 |reached target block errors\n",
      "      0.0 | 3.3490e-03 | 2.4017e-02 |      126421 |    37748736 |          787 |       32768 |        19.3 |reached max iterations\n",
      "      2.0 | 1.0733e-03 | 6.5613e-03 |       40517 |    37748736 |          215 |       32768 |        19.3 |reached max iterations\n",
      "      4.0 | 6.9017e-04 | 3.8452e-03 |       26053 |    37748736 |          126 |       32768 |        19.3 |reached max iterations\n",
      "      6.0 | 5.6526e-04 | 3.4485e-03 |       21338 |    37748736 |          113 |       32768 |        19.3 |reached max iterations\n",
      "      8.0 | 6.3758e-04 | 4.3640e-03 |       24068 |    37748736 |          143 |       32768 |        19.3 |reached max iterations\n",
      "     10.0 | 7.0741e-04 | 5.6763e-03 |       26704 |    37748736 |          186 |       32768 |        19.3 |reached max iterations\n"
     ]
    }
   ],
   "source": [
    "# Range of SNR (dB)\n",
    "snr_range_cest = np.linspace(ebno_db_min_cest, ebno_db_max_cest, num_steps)\n",
    "snr_range_perf_csi = np.linspace(ebno_db_min_perf_csi, ebno_db_max_perf_csi, num_steps)\n",
    "\n",
    "def run_idd_sim(snr_range, perfect_csi_rayleigh):\n",
    "    lmmse = NonIddModel(detector=\"lmmse\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    k_best = NonIddModel(detector=\"k-best\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    ep = NonIddModel(detector=\"ep\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    idd2 = IddModel(num_idd_iter=2, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    idd3 = IddModel(num_idd_iter=3, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "\n",
    "    ber_lmmse, bler_lmmse = sim_ber(lmmse,\n",
    "                                    snr_range,\n",
    "                                    batch_size=batch_size,\n",
    "                                    max_mc_iter=num_iter,\n",
    "                                    num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_ep, bler_ep = sim_ber(ep,\n",
    "                              snr_range,\n",
    "                              batch_size=batch_size,\n",
    "                              max_mc_iter=num_iter,\n",
    "                              num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_kbest, bler_kbest = sim_ber(k_best,\n",
    "                                    snr_range,\n",
    "                                    batch_size=batch_size,\n",
    "                                    max_mc_iter=num_iter,\n",
    "                                    num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_idd2, bler_idd2 = sim_ber(idd2,\n",
    "                                  snr_range,\n",
    "                                  batch_size=batch_size,\n",
    "                                  max_mc_iter=num_iter,\n",
    "                                  num_target_block_errors=int(batch_size * num_iter * 0.1)\n",
    "                                  )\n",
    "\n",
    "    ber_idd3, bler_idd3 = sim_ber(idd3,\n",
    "                                  snr_range,\n",
    "                                  batch_size=batch_size,\n",
    "                                  max_mc_iter=num_iter,\n",
    "                                  num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    return bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3\n",
    "\n",
    "\n",
    "BLER = {}\n",
    "\n",
    "# Perfect CSI\n",
    "bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3 = run_idd_sim(snr_range_perf_csi, perfect_csi_rayleigh=True)\n",
    "BLER['Perf. CSI / LMMSE'] = bler_lmmse\n",
    "BLER['Perf. CSI / EP'] = bler_ep\n",
    "BLER['Perf. CSI / K-Best'] = bler_kbest\n",
    "BLER['Perf. CSI / IDD2'] = bler_idd2\n",
    "BLER['Perf. CSI / IDD3'] = bler_idd3\n",
    "\n",
    "# Estimated CSI\n",
    "bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3 = run_idd_sim(snr_range_cest, perfect_csi_rayleigh=False)\n",
    "BLER['Ch. Est. / LMMSE'] = bler_lmmse\n",
    "BLER['Ch. Est. / EP'] = bler_ep\n",
    "BLER['Ch. Est. / K-Best'] = bler_kbest\n",
    "BLER['Ch. Est. / IDD2'] = bler_idd2\n",
    "BLER['Ch. Est. / IDD3'] = bler_idd3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the simulation results and observe that IDD outperforms the non-iterative methods by about 1 dB in the scenario with iid Rayleigh fading channels and perfect CSI. In the scenario with 3GPP UMa channels and estimated CSI, IDD performs slightly better than K-best, at considerably lower runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-10T22:23:11.996961Z",
     "iopub.status.busy": "2025-03-10T22:23:11.996739Z",
     "iopub.status.idle": "2025-03-10T22:23:12.710670Z",
     "shell.execute_reply": "2025-03-10T22:23:12.710010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(16,7))\n",
    "fig.suptitle(f\"{n_ue}x{NUM_RX_ANT} MU-MIMO UL | {2**num_bits_per_symbol}-QAM\")\n",
    "\n",
    "## Perfect CSI Rayleigh\n",
    "ax[0].set_title(\"Perfect CSI iid. Rayleigh\")\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / LMMSE'], 'x-', label='LMMSE', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / EP'], 'o--', label='EP', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / K-Best'], 's-.', label='K-Best', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / IDD2'], 'd:', label=r'IDD $I=2$', c='C1')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / IDD3'], 'd:', label=r'IDD $I=3$', c='C2')\n",
    "\n",
    "ax[0].set_xlabel(r\"$E_b/N0$\")\n",
    "ax[0].set_ylabel(\"BLER\")\n",
    "ax[0].set_ylim((1e-4, 1.0))\n",
    "ax[0].legend()\n",
    "ax[0].grid(True)\n",
    "\n",
    "## Estimated CSI Rayleigh\n",
    "ax[1].set_title(\"Estimated CSI 3GPP UMa\")\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / LMMSE'], 'x-', label='LMMSE', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / EP'], 'o--', label='EP', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / K-Best'], 's-.', label='K-Best', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / IDD2'], 'd:', label=r'IDD $I=2$', c='C1')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / IDD3'], 'd:', label=r'IDD $I=3$', c='C2')\n",
    "\n",
    "ax[1].set_xlabel(r\"$E_b/N0$\")\n",
    "ax[1].set_ylabel(\"BLER\")\n",
    "ax[1].set_ylim((1e-3, 1.0))\n",
    "ax[1].legend()\n",
    "ax[1].grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion-Optimizing IDD with Machine Learning\n",
    "Recent work [4] showed that IDD can be significantly improved by deep-unfolding, which applies machine learning to automatically tune hyperparameters of classical algorithms. The proposed *Deep-Unfolded Interleaved Detection and Decoding* method showed performance gains of up to 1.4 dB at the same computational complexity. A link to the simulation code is available in the [\"Made with Sionna\"](https://nvlabs.github.io/sionna/made_with_sionna.html#duidd-deep-unfolded-interleaved-detection-and-decoding-for-mimo-wireless-systems) section. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comments\n",
    "\n",
    "- As discussed in [3], IDD receivers with a non-resetting decoder converge faster than with resetting decoders. However, a resetting decoder (which does not forward `msg_vn`) might perform slightly better for a large number of message passing decoding iterations. Among other quantities, a scaling of the forwarded decoder state is optimized in the DUIDD receiver [4].\n",
    "- With estimated channels, we observed that the MMSE-PIC output LLRs become large, much larger as with non-iterative receive processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List of References\n",
    "\n",
    "[1] B. Hochwald and S. Ten Brink, [*\"Achieving near-capacity on a multiple-antenna channel,\"*](https://ieeexplore.ieee.org/abstract/document/1194444) IEEE Trans. Commun., vol. 51, no. 3, pp. 389–399, Mar. 2003.\n",
    "\n",
    "[2] C. Studer, S. Fateh, and D. Seethaler, [*\"ASIC implementation of soft-input soft-output MIMO detection\n",
    "using MMSE parallel interference cancellation,\"*](https://ieeexplore.ieee.org/abstract/document/5779722) IEEE Journal of Solid-State Circuits, vol. 46, no. 7, pp. 1754–1765, Jul. 2011.\n",
    "\n",
    "[3] W.-C. Sun, W.-H. Wu, C.-H. Yang, and Y.-L. Ueng, [*\"An iterative detection and decoding receiver for LDPC-coded MIMO systems,\"*](https://ieeexplore.ieee.org/abstract/document/7272776) IEEE Trans. Circuits Syst. I, vol. 62, no. 10, pp. 2512–2522, Oct. 2015.\n",
    "\n",
    "[4] R. Wiesmayr, C. Dick, J. Hoydis, and C. Studer, [*\"DUIDD: Deep-unfolded interleaved detection and decoding for MIMO wireless systems,\"*](https://arxiv.org/abs/2212.07816) in Asilomar Conf. Signals, Syst., Comput., Oct. 2022."
   ]
  }
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